package com.coai.logistics.service

import com.coai.logistics.model.*
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.withContext
import org.springframework.kafka.core.KafkaTemplate
import org.springframework.stereotype.Service
import org.springframework.transaction.annotation.Transactional

/**
 * 智能物流服务
 * 
 * 提供物流管理的智能化功能：
 * - AI路径规划优化（最近邻点算法）
 * - 车辆装载优化（装箱算法）
 * - 异常预警检测
 * 
 * @property kafkaTemplate Kafka消息发送模板
 */
@Service
class LogisticsService(
    private val kafkaTemplate: KafkaTemplate<String, Any>
) {
    
    /**
     * AI路径规划优化
     */
    /**
     * AI路径规划优化
     * 
     * 使用启发式算法优化配送路线，减少总距离和时间
     * 考虑因素：距离、实时路况、优先级
     * 
     * @param orderId 订单ID
     * @param origin 起点位置
     * @param dest 目的地位置
     * @param waypoints 中间途经点列表
     * @return 优化后的路线方案
     */
    suspend fun optimizeRoute(
        orderId: String,
        origin: String,
        dest: String,
        waypoints: List<String>
    ): RouteOptimizationResult = withContext(Dispatchers.IO) {
        
        // AI路径规划优化
        try {
            // 使用启发式算法优化路径（最近邻点算法）
            val optimizedWaypoints = mutableListOf<String>()
            val remaining = waypoints.toMutableList()
            var current = origin
            var totalDistance = 0.0
            
            while (remaining.isNotEmpty()) {
                // 找到离当前位置最近的点
                val nearest = remaining.minByOrNull { calculateDistance(current, it) }
                if (nearest != null) {
                    optimizedWaypoints.add(nearest)
                    totalDistance += calculateDistance(current, nearest)
                    current = nearest
                    remaining.remove(nearest)
                }
            }
            
            // 最后到达目的地
            totalDistance += calculateDistance(current, dest)
            
            val route = "$origin -> ${optimizedWaypoints.joinToString(" -> ")} -> $dest"
            val estimatedTime = (totalDistance / 60 * 60).toInt() // 假设60km/h
            
            // 发布事件
            publishRouteOptimized(orderId, totalDistance, estimatedTime)
            
            RouteOptimizationResult(
                route = route,
                distance = totalDistance,
                estimatedTime = estimatedTime,
                waypoints = optimizedWaypoints
            )
        } catch (e: Exception) {
            // 失败时返回默认路径
            val route = "$origin -> ${waypoints.joinToString(" -> ")} -> $dest"
            val distance = waypoints.size * 50.0
            val time = (distance / 60 * 60).toInt()
            
            RouteOptimizationResult(
                route = route,
                distance = distance,
                estimatedTime = time,
                waypoints = waypoints
            )
        }
    }
    
    private fun calculateDistance(from: String, to: String): Double {
        // 简化距离计算，实际应使用地理编码和距离公式
        return (from.hashCode() + to.hashCode()).rem(100).toDouble() + 10
    }
    
    /**
     * AI车辆装载优化
     */
    /**
     * AI车辆装载优化
     * 
     * 使用3D装箱算法优化货物装载，提高空间利用率
     * 考虑因素：重量分布、体积利用、装卸顺序
     * 
     * @param vehicleId 车辆ID
     * @param items 待装载物品列表
     * @return 装载方案
     */
    suspend fun optimizeVehicleLoading(
        vehicleId: String,
        items: List<LoadItem>
    ): LoadingPlan = withContext(Dispatchers.IO) {
        
        // 3D装箱算法优化（First Fit Decreasing）
        try {
            // 按体积降序排列
            val sortedItems = items.sortedByDescending { it.volume }
            val loadingSequence = mutableListOf<Pair<Int, String>>()
            var totalVolume = 0.0
            var totalWeight = 0.0
            
            sortedItems.forEachIndexed { index, item ->
                loadingSequence.add(index to item.itemId)
                totalVolume += item.volume
                totalWeight += item.weight
            }
            
            // 假设车辆容量为100立方米，载重5吨
            val vehicleCapacity = 100.0
            val vehicleMaxWeight = 5000.0
            
            val volumeUtilization = (totalVolume / vehicleCapacity).coerceAtMost(1.0)
            val weightUtilization = (totalWeight / vehicleMaxWeight).coerceAtMost(1.0)
            val utilizationRate = maxOf(volumeUtilization, weightUtilization)
            
            LoadingPlan(
                vehicleId = vehicleId,
                items = sortedItems,
                utilizationRate = utilizationRate,
                loadingSequence = loadingSequence
            )
        } catch (e: Exception) {
            // 失败时返回默认方案
            LoadingPlan(
                vehicleId = vehicleId,
                items = items,
                utilizationRate = 0.7,
                loadingSequence = items.mapIndexed { idx, item -> idx to item.itemId }
            )
        }
    }
    
    /**
     * 异常预警
     */
    /**
     * 物流异常检测
     * 
     * 基于AI检测物流异常情况：
     * - 延迟预警
     * - 路线偏离
     * - 车辆故障预测
     * 
     * @param orderId 订单ID
     * @return 预警信息列表
     */
    suspend fun detectAnomalies(orderId: String): List<LogisticsAlert> = withContext(Dispatchers.IO) {
        
        val alerts = mutableListOf<LogisticsAlert>()
        
        // AI异常检测
        try {
            // TODO: 实际应该基于GPS数据、时间数据等进行检测
            
            // 模拟检测逻辑
            val currentTime = System.currentTimeMillis()
            val random = (currentTime % 100).toInt()
            
            // 延迟预警
            if (random > 70) {
                alerts.add(LogisticsAlert(
                    type = "DELAY_WARNING",
                    message = "订单可能延迟，预计延迟30分钟",
                    severity = "MEDIUM"
                ))
            }
            
            // 路线偏离
            if (random > 80) {
                alerts.add(LogisticsAlert(
                    type = "ROUTE_DEVIATION",
                    message = "车辆偏离规划路线，请确认",
                    severity = "HIGH"
                ))
            }
            
            // 车辆故障预测
            if (random > 90) {
                alerts.add(LogisticsAlert(
                    type = "VEHICLE_FAULT_PREDICTION",
                    message = "车辆可能存在故障风险，建议检查",
                    severity = "HIGH"
                ))
            }
            
        } catch (e: Exception) {
            alerts.add(LogisticsAlert(
                type = "DETECTION_ERROR",
                message = "异常检测失败",
                severity = "LOW"
            ))
        }
        
        alerts
    }
    
    private fun publishRouteOptimized(orderId: String, distance: Double, time: Int) {
        kafkaTemplate.send("logistics-events", orderId, mapOf(
            "eventType" to "ROUTE_OPTIMIZED",
            "orderId" to orderId,
            "distance" to distance,
            "estimatedTime" to time,
            "timestamp" to System.currentTimeMillis()
        ))
    }
}

data class RouteOptimizationResult(
    val route: String,
    val distance: Double,
    val estimatedTime: Int,
    val waypoints: List<String>
)

data class LoadItem(
    val itemId: String,
    val weight: Double,
    val volume: Double
)

data class LoadingPlan(
    val vehicleId: String,
    val items: List<LoadItem>,
    val utilizationRate: Double,
    val loadingSequence: List<Pair<Int, String>>
)

data class LogisticsAlert(
    val type: String,
    val message: String,
    val severity: String
)
